Learning models from data: the set membership approach

Author

Milanese, Mario

Author_Institution

Dipt. di Autom. e Inf., Politecnico di Torino, Italy

Volume

1

fYear

1998

fDate

21-26 Jun 1998

Firstpage

178

Abstract

The problem of identifying complex linear systems from noise corrupted data is investigated, considering that only approximate models can be estimated and the effects of unmodeled dynamics have to be accounted for. The paper presents a unified view of the set membership identification theory, as recently evolved by the author et al. (1997), aiming to deliver not only a model of the system to be identified, but also a measure of its approximation. Optimality and convergence results are reported, related to identification problems for different settings of experimental conditions and noise assumptions

Keywords

approximation theory; convergence of numerical methods; identification; large-scale systems; linear systems; optimisation; set theory; approximation; complex linear systems; convergence; identification; learning models; noise corrupted data; optimisation; set membership; set theory; unmodeled dynamics; Control design; Convergence; Decision making; Fault detection; Fault diagnosis; Filtering; Linear systems; Noise measurement; Samarium; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1998. Proceedings of the 1998

Conference_Location

Philadelphia, PA

ISSN

0743-1619

Print_ISBN

0-7803-4530-4

Type

conf

DOI

10.1109/ACC.1998.694653

Filename

694653